EthGAN: Improving Ethereum Account Classification Accuracy via Data Augmentation

Abstract

Recently, with the prevalent adoption of blockchain in the financial system, there has been an increasing of anomaly activities such as ponzi schemes, gambling and phishing fraud on Ethereum platforms, and an effective account classification method is urgently required. The existing account classification methods on Ethereum with high accuracy require a learning system to be trained with balanced datasets. However, the distribution of annotated labels for account identities published on third-party sites is relatively imbalanced. Therefore, in this paper, We propose a EthGAN framework which includes a high-dimensional node feature representation module and a few-shot account data augment module to improve the accuracy and robustness at imbalanced datasets. The high-dimensional node feature representation module captures features from statistical, temporal, and transaction structure, and the few-shot account data augmentation module based on generative adversarial network models generate few-shot samples to improve the diversity and representativeness of the training datasets. We conduct extensive experiments to evaluate the performance of our proposed EthGAN framework on real-world Ethereum transaction data. The average classification effect of our method is 10+% higher than that of existing methods. Experimental results demonstrate that our method outperforms state-of-the-art methods in Ethereum account classification.